Journal articles: 'Views selection'

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Haider, Mohammad, and T. V. Vijay Kumar. "Query Frequency based View Selection." International Journal of Business Analytics 4, no. 1 (January 2017): 36–55. http://dx.doi.org/10.4018/ijban.2017010103.

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View selection deals with the selection of appropriate sets of views capable of improving the response times for queries while conforming to space constraints. Materializing all views is infeasible, as the number of possible views is exponential with respect to the number of dimensions and, thus, would not fit within the available storage space. Further, optimal view selection is an NP-Complete problem. Thus, the only remaining alternative is to select a subset of views that reduce the query response time and fit within the available space for materialization. The most fundamental greedy view selection algorithm HRUA considers the size parameter for computing the Top-K views for materialization. In each iteration, it computes the benefit, with respect to size, of all non-selected views, and selects the one entailing the highest benefit for materialization. Though these selected views may be beneficial in respect of their size, they may not be capable of answering large numbers of future queries thereby becoming an unnecessary space overhead. Existing query frequency based view selection algorithms, which address this problem, have been compared in this paper. Experimental results show that each of these algorithms, in comparison to HRUA, are able to select fairly good quality views that provide answers to comparatively greater numbers of queries. Materializing these selected views would facilitate the business decision making process.

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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection using Marriage in Honey Bees Optimization." International Journal of Natural Computing Research 5, no. 3 (July 2015): 1–25. http://dx.doi.org/10.4018/ijncr.2015070101.

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Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

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Hurley, Catherine B. "Graphical Selection of Data Views." Journal of Computational and Graphical Statistics 9, no. 3 (September 2000): 558. http://dx.doi.org/10.2307/1390946.

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Hurley, Catherine B. "Graphical Selection of Data Views." Journal of Computational and Graphical Statistics 9, no. 3 (September 2000): 558–81. http://dx.doi.org/10.1080/10618600.2000.10474899.

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Prakash, Jay, and T. V. Vijay Kumar. "Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm." International Journal of Artificial Intelligence and Machine Learning 9, no. 2 (July 2019): 1–21. http://dx.doi.org/10.4018/ijaiml.2019070101.

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A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.

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Mouna, Mustapha, Ladjel Bellatreche, and Narhimene Boustia. "ProRes: Proactive re-selection of materialized views." Computer Science and Information Systems, no. 00 (2022): 3. http://dx.doi.org/10.2298/csis210606003m.

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Materialized View Selection is one of the most studied problems in the database field, covering SQL and NoSQL technologies as well as different deployment infrastructures (centralized, parallel, cloud). This problem has become more complex with the arrival of data warehouses, being coupled with the physical de sign phase that aims at optimizing query performance. Selecting the best set of materialized views to optimize query performance is a challenging task. Given their importance and the complexity of their selection, several research efforts both from academia and industry have been conducted. Results are promising - some solutions are being implemented by commercial and open-source DBMSs -, but they do not factor in the following properties of nowadays analytical queries: (i) large scale queries, (ii) their dynamicity, and (iii) their high interaction. Studies to date fail to consider that complete set of properties. Considering the three properties simultaneously is crucial regarding today?s analytical requirements, which involve dynamic and interactive queries. In this paper, we first present a concise state of the art of the materialized view selection problem (VSP) by analyzing its ecosystem. Secondly, we propose a proactive re-selection approach that considers the three properties concurrently. It features twomain phases: offline and online. In the offline phase, we manage a set of the first queries based on a given threshold _ by selecting materialized views through a hypergraph structure. The second phase manages the addition of new queries by scheduling them, updates the structure of the hypergraph, and selects new views by eliminating the least beneficial ones. Finally, extensive experiments are conducted using the Star Schema Benchmark data set to evaluate the effectiveness and efficiency of our approach.

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Wu, Xiaoying, and Dimitri Theodoratos. "Template-Based Bitmap View Selection for Optimizing Queries Over Tree Data." International Journal of Cooperative Information Systems 25, no. 03 (September 2016): 1650005. http://dx.doi.org/10.1142/s0218843016500052.

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Developing and exploiting flexible techniques for optimizing the evaluation of queries over loosely structured data (e.g. tree or graph databases) is of crucial importance for modern database applications. In this context, we consider a new type of views which can be materialized as compressed bitmaps over tree data. We introduce the concept of view structural template to define classes of views. We then define and address a novel view selection problem (called view class selection (VCS) problem) where the goal is to select classes of bitmap views in order to optimize the overall evaluation cost of all tree pattern queries (TPQs) that can be issued against a database while satisfying a space constraint and ensuring that all the TPQs can be answered using exclusively the materialized views. We show that the VCS problem is NP-hard and we design two heuristic greedy algorithms which iteratively generate new batches of candidate view classes and make them available for selection. Each algorithm uses a different view class expansion technique to enable the systematic generation of candidate view classes from classes with smaller templates. We run extensive experiments to evaluate both the effectiveness of the algorithms and their efficiency on real, benchmark and synthetic datasets. Our algorithms are able to suggest high quality selections of view classes in a reasonable amount of time.

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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection using Artificial Bee Colony Optimization." International Journal of Intelligent Information Technologies 13, no. 1 (January 2017): 26–49. http://dx.doi.org/10.4018/ijiit.2017010102.

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Data warehouse is an essential component of almost every modern enterprise information system. It stores huge amount of subject-oriented, time-stamped, non-volatile and integrated data. It is highly required of the system to respond to complex online analytical queries posed against its data warehouse in seconds for efficient decision making. Optimization of online analytical query processing (OLAP) could substantially minimize delays in query response time. Materialized view is an efficient and effective OLAP query optimization technique to minimize query response time. Selecting a set of such appropriate views for materialization is referred to as view selection, which is a nontrivial task. In this regard, an Artificial Bee Colony (ABC) based view selection algorithm (ABCVSA), which has been adapted by incorporating N-point and GBFS based N-point random insertion operations, to select Top-K views from a multidimensional lattice is proposed. Experimental results show that ABCVSA performs better than the most fundamental view selection algorithm HRUA. Thus, the views selected using ABCVSA on materialization would reduce the query response time of OLAP queries and thereby aid analysts in arriving at strategic business decisions in an effective manner.

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Zhou, Li Juan, Hai Jun Geng, and Ming Sheng Xu. "Materialized View Selection in the Data Warehouse." Applied Mechanics and Materials 29-32 (August 2010): 1133–38. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1133.

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A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. The goal is to select an appropriate set of views that minimizes sum of the query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In this article, we present an improved PGA algorithm to accomplish the view selection problem; the experiments show that our proposed algorithm shows it’s superior.

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Bagale, Purushottam, and Shashidhar Ram Joshi. "Optimal Materialized View Management in Distributed Environment Using Random Walk Approach." Journal of Advanced College of Engineering and Management 1 (May 13, 2016): 67. http://dx.doi.org/10.3126/jacem.v1i0.14923.

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Materialized View selection and maintenance is a critical problem in many applications. In large databases particularly in distributed database, query response time plays an important role as timely access to information and it is the basic requirement of successful business application. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Materialized views selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Selecting a suitable set of views that minimizes the total cost associated with the materialized views is the key component in distributed database environment. Several solutions have been proposed in the literature to solve this problem. However, most studies do not encompass search time, storage constrains and maintenance cost. In this research work two algorithms are depicted; first for materialized view selection and maintenance in distributed environment where database is distributed, Second algorithm is for node selection in distributed environment. Journal of Advanced College of Engineering and Management, Vol.1, 2015, 69-75

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Prakash, Jay, and T. V. Vijay Kumar. "A Multi-Objective Approach for Materialized View Selection." International Journal of Operations Research and Information Systems 10, no. 2 (April 2019): 1–19. http://dx.doi.org/10.4018/ijoris.2019040101.

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In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection Using Bumble Bee Mating Optimization." International Journal of Decision Support System Technology 9, no. 3 (July 2017): 1–27. http://dx.doi.org/10.4018/ijdsst.2017070101.

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Decision support systems (DSS) constitute one of the most crucial components of almost every corporation's information system. Data warehouse provides the DSS with massive volumes of quality corporate data for analysis. On account of the volume of corporate data, its processing time of on-line analytical queries is huge (in hours and days). Materialized views have been used to substantially improve query performance. Nevertheless, selecting appropriate sets of materialized views is an NP-Complete problem. In this paper, a new discrete bumble bee mating inspired view selection algorithm (BBMVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental results show that BBMVSA was able to select fairly good quality Top-K views incurring a lower TVEC. Materialization of the selected views would improve the overall data analysis of DSS and would facilitate the decision making process.

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GUION, ROBERT M. "CHANGING VIEWS FOR PERSONNEL SELECTION RESEARCH." Personnel Psychology 40, no. 2 (June 1987): 199–213. http://dx.doi.org/10.1111/j.1744-6570.1987.tb00601.x.

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Meagher, Thomas R. "Sexual Selection: A Survey of Views." Ecology 70, no. 2 (April 1989): 521–22. http://dx.doi.org/10.2307/1937561.

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Tsai, Yu-Ju, Yu-Lun Liu, Ming Ouhyoung, and Yung-Yu Chuang. "Attention-Based View Selection Networks for Light-Field Disparity Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12095–103. http://dx.doi.org/10.1609/aaai.v34i07.6888.

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This paper introduces a novel deep network for estimating depth maps from a light field image. For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation. By exploring the symmetric property of light field views, we enforce symmetry in the attention map and further improve accuracy. With the attention map, our architecture utilizes all views more effectively and efficiently. Experiments show that the proposed method achieves state-of-the-art performance in terms of accuracy and ranks the first on a popular benchmark for disparity estimation for light field images.

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Luo, Lei, Rong Xin Jiang, Xiang Tian, and Yao Wu Chen. "Reference Viewpoints Selection for Multi-View Video Plus Depth Coding Based on the Network Bandwidth Constraint." Applied Mechanics and Materials 303-306 (February 2013): 2134–38. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2134.

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In multi-view video plus depth (MVD) coding based free viewpoint video applications, a few reference viewpoints’ texture and depth videos should be compressed and transmitted at the server side. At the terminal side, the display view videos could be the decoded reference view videos or the virtual viewpoints’ videos which are synthesized by DIBR technology. The entire video quality of all display views are decided by the number of reference viewpoints and the compression distortion of each reference viewpoint’s texture and depth videos. This paper studies the impact of the reference viewpoints selection on the entire video quality of all display views. The results show that depending on the available network bandwidth, the MVD coding requires different selections of reference viewpoints to maximize the entire video quality of all display views.

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Mohseni, Mohsen, and Mohammad Karim Sohrabi. "MVPP-Based Materialized View Selection in Data Warehouses Using Simulated Annealing." International Journal of Cooperative Information Systems 29, no. 03 (August 28, 2020): 2050001. http://dx.doi.org/10.1142/s021884302050001x.

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The process of extracting data from different heterogeneous data sources, transforming them into an integrated, unified and cleaned repository, and storing the result as a single entity leads to the construction of a data warehouse (DW), which facilitates access to data for the users of information systems and decision support systems. Due to their enormous volumes of data, processing of analytical queries of decision support systems need to scan very large amounts of data, which has a negative effect on the systems’ response time. Because of the special importance of online analytical processing (OLAP) in these systems, to enhance the performance and improve the query response time of the system, an appropriate number of views of the DW are selected for materialization and will be utilized for responding to the analytical queries, instead of direct access to the base relations. Memory constraint and views maintenance overhead are two main limitations that make it impossible, in most cases, to materialize all views of the DW. Selecting a proper set of views of DW for materialization, called materialized view selection (MVS) problem, is an important research issue that has been focused in various papers. In this paper, we have proposed a method, called P-SA, to select an appropriate set of views using an improved version of simulated annealing (SA) algorithm that utilizes a proper neighborhood selection strategy. P-SA uses the multiple view processing plan (MVPP) structure for selecting the views. Data and queries of a benchmark DW have been used in experimental results for evaluating the introduced method. The experimental results show better performance of the P-SA compared to other SA-based MVS methods for increasing the number of queries, in terms of the total cost of view maintenance and query processing. Moreover, the total cost of queries in the P-SA is also better than the other important SA-based MVS methods of the literature when the size of the DW is increased.

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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Swap Operator Based Particle Swarm Optimization." International Journal of Distributed Artificial Intelligence 13, no. 1 (January 2021): 58–73. http://dx.doi.org/10.4018/ijdai.2021010103.

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The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

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Chen, Jia, Hong Wei Chen, and Xin Rong Hu. "Simulation for View Selection in Data Warehouse." Advanced Materials Research 748 (August 2013): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1028.

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On-Line Analytical Processing (OLAP) tools are frequently used in business, science and health to extract useful knowledge from massive databases. An important and hard optimization problem in OLAP data warehouses is the view selection problem, consisting of selecting a set of aggregate views of the data for speeding up future query processing. We apply one n Estimation of Distribution Algorithms (EDAs) to view selection under a size constraint. Our emphasis is to determine the suitability of the combination of EDAs with constraint handling to the view selection problem, compared to a widely used genetic algorithm. The EDAs are competitive with the genetic algorithm on a variety of problem instances, often finding approximate optimal solutions in a reasonable amount of time.

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Cheng, Jian, Yanguang Wan, Dexin Zuo, Cuixia Ma, Jian Gu, Ping Tan, Hongan Wang, Xiaoming Deng, and Yinda Zhang. "Efficient Virtual View Selection for 3D Hand Pose Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 419–26. http://dx.doi.org/10.1609/aaai.v36i1.19919.

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3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand. In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth. We propose to automatically select multiple virtual viewpoints for pose estimation and fuse the results of all and find this empirically delivers accurate and robust pose estimation. In order to select most effective virtual views for pose fusion, we evaluate the virtual views based on the confidence of virtual views using a light-weight network via network distillation. Experiments on three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves very competitive performance on Hands2019-Task1, and our proposed virtual view selection and fusion module is both effective for 3D hand pose estimation.

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Ratkevičius, Donatas, Česlovas Ratkevičius, and Rimvydas Skyrius. "ERP SELECTION CRITERIA: THEORETICAL AND PRACTICAL VIEWS." Ekonomika 91, no. 2 (January 1, 2012): 97–116. http://dx.doi.org/10.15388/ekon.2012.0.893.

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This article deals with the problems of Enterprise Resource Planning (ERP) system selection as the initial and vital phase of ERP system implementation.Firstly, the paper presents an analysis of different classifications of the fundamental criteria for the ERP system selection process, published in scholar sources, and defines two main groups – software-related, and implementation-related ERP selection criteria. Secondly, combining theoretical and practical approaches, the most significant ERP system selection criteria of both groups are identified and reviewed by analyzing and interpreting their definitions and differences. The study is complemented by adding practical/statistical findings produced by different consultancies.The paper concludes that there is no standard classification of ERP selection criteria. They are classified mostly on the basis of scientists’ research interests.The significance of ERP system functionality as the principal software-related ERP selection criterion is emphasized. Eleven other criteria were defined as important to consider, such as the total costs of the ERP implementation project, vendor reputation, ERP reliability, ease of integration with other systems, technology advance, scalability, upgrading ability, customization / parameterization possibilities; ease of use; flexibility and modularity.The importance of all-round knowledge for a successful ERP implementation is emphasized, including ERP software functionality, project and change management, business processes, organization of training etc. All these areas are closely connected with implementation-related ERP selection factors: organisational fit, end-user readiness, training, system support quality, and the overall ERP implementation success which is predefined by the complexity of business environment as well as the level of business transformation, defined by technological changes.Finally, it is stated that for creating a decision support system which would automate the ERP selection process, the quantitative analysis of ERP selection criteria would be required.

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Kumar, T. V. Vijay. "Answering query-based selection of materialised views." International Journal of Information and Decision Sciences 5, no. 1 (2013): 103. http://dx.doi.org/10.1504/ijids.2013.052015.

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Pollitzer, William S. "Two views of natural selection and evolution." Reviews in Anthropology 14, no. 3 (June 1987): 187–94. http://dx.doi.org/10.1080/00988157.1987.9977825.

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Feinerer, Ingo, Enrico Franconi, and Paolo Guagliardo. "Lossless Selection Views under Conditional Domain Constraints." IEEE Transactions on Knowledge and Data Engineering 27, no. 2 (February 1, 2015): 504–17. http://dx.doi.org/10.1109/tkde.2014.2334327.

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Jeske, Debora, Annalisa Setti, and Daisy Beth Gibbons. "Views on aging in selection: HR implications." Strategic HR Review 18, no. 5 (October 14, 2019): 227–32. http://dx.doi.org/10.1108/shr-04-2019-0029.

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Purpose It is well-known that stereotypes on aging and perceptions about the suitability of certain jobs for certain age groups can influence performance ratings. However, it is unclear whether and how subjective views on aging are associated with judgment on someone else’s performance. The purpose of this study is to explore the role of aging perceptions and images of aging on performance ratings for a fictitious set of male candidates with different age and job profiles. Ratings of interest were job suitability, developmental potential, interpersonal skills and performance capacity. Design/methodology/approach Using an online survey format, data was collected from 203 Irish and UK employees to assess how they evaluated different fictitious candidates for a local development committee. The age and mentorship status of the candidates were also manipulated. Findings The age or mentoring status of the candidate did not play a significant role in how they were rated. Multiple regression analyses indicated, however, that participants’ aging perceptions and aging images had a significantly positive influence on how they rated the fictitious candidates (after controlling for participant variables such as age and experience). However, positive images of aging and aging perceptions on the part of the participants predicted more positive overall job suitability ratings, developmental potential, interpersonal skills and performance capacity. When the participants had more negative views on aging, they would also allocate lower ratings. Originality/value The results indicate that employee attitudes about aging play a role in how they will rate others. Given the importance of potential rating bias, the authors propose a number of training interventions that human resource professionals may be able to carry out to positively shape the informational basis for more negative aging attitudes.

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Vázquez, Pere-Pau, Miquel Feixas, Mateu Sbert, and Antoni Llobet. "Realtime automatic selection of good molecular views." Computers & Graphics 30, no. 1 (February 2006): 98–110. http://dx.doi.org/10.1016/j.cag.2005.10.022.

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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization." International Journal of Applied Evolutionary Computation 11, no. 3 (July 2020): 50–67. http://dx.doi.org/10.4018/ijaec.2020070104.

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A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of data warehouses. Selecting a suitable subset of views that deceases the response time of analytical queries and also fit within available storage space for materialization is a crucial research concern in the context of a data warehouse design. This problem, referred to as view selection, is shown to be NP-Hard. Swarm intelligence have been widely and successfully used to solve such problems. In this paper, a discrete variant of particle swarm optimization algorithm, i.e. self-adaptive perturbation operator based particle swarm optimization (SPOPSO), has been adapted to solve the view selection problem. Accordingly, SPOPSO-based view selection algorithm (SPOPSOVSA) is proposed. SPOPSOVSA selects the Top-K views in a multidimensional lattice framework. Further, the proposed algorithm is shown to perform better than the view selection algorithm HRUA.

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LEE, MINSOO, and JOACHIM HAMMER. "SPEEDING UP MATERIALIZED VIEW SELECTION IN DATA WAREHOUSES USING A RANDOMIZED ALGORITHM." International Journal of Cooperative Information Systems 10, no. 03 (September 2001): 327–53. http://dx.doi.org/10.1142/s0218843001000370.

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A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-based approaches using heuristics. Our analysis shows that the algorithm consistently yields a solution that lies within 10% of the optimal query benefit while at the same time exhibiting only a linear increase in execution time. We have implemented a prototype version of our algorithm which is used to simulate the measurements used in the analysis of our approach.

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Kumar, Amit, and T. V. Vijay Kumar. "Materialized View Selection Using Set Based Particle Swarm Optimization." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 3 (July 2018): 18–39. http://dx.doi.org/10.4018/ijcini.2018070102.

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A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

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Arun, Biri, and T. V. Vijay Kumar. "Materialized View Selection using Improvement based Bee Colony Optimization." International Journal of Software Science and Computational Intelligence 7, no. 4 (October 2015): 35–61. http://dx.doi.org/10.4018/ijssci.2015100103.

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In the present information age, data and information are vital not just for the survival of any corporate entity, but also to provide it with an edge over its competitors. Data warehouses have become the foundational databases of almost every corporation. However, extracting new information from these data warehouses takes hours, and even days, which is practically unacceptable. Materialized views have been popularly used to facilitate fast information extraction. However, the selection of appropriate views, which significantly accelerate information synthesis is an NP-Complete problem. The aim of this paper is to select near optimal sets of views for materialization using the improvement bee colony optimization algorithm. The experimental results indicate that the improvement bee colony optimization algorithm performs better than the constructive bee colony optimization algorithm and the fundamental view selection algorithm HRUA. The views thus selected would significantly minimize the response time of analytical queries, when materialized, resulting in efficient strategic decision making.

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Boukorca, Ahcene, Ladjel Bellatreche, Sid-Ahmed Benali Senouci, and Zoé Faget. "Coupling Materialized View Selection to Multi Query Optimization." International Journal of Data Warehousing and Mining 11, no. 2 (April 2015): 62–84. http://dx.doi.org/10.4018/ijdwm.2015040104.

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Materialized views are queries whose results are stored and maintained in order to facilitate access to data in their underlying base tables of extremely large databases. Selecting the best materialized views for a given query workload is a hard problem. Studies on view selection have considered sharing common sub expressions and other multi-query optimization techniques. Multi-Query Optimization is a well-studied domain in traditional and advanced databases. It aims at optimizing a workload of queries by finding and reusing common sub-expression between queries. Finding the best shared expression is known as a NP-hard problem. The shared expressions usually identified by graph structure have been used to be candidate for materialized views. This shows the strong interdependency between the problems of materialized view selection (PVS) and multi query optimization (PMQO), since the PVS uses the graph structure of the PMQO. Exploring the existing works on PVS considering the interaction between PVS and PMQO figures two main categories of studies: (i) those considering the PMQO as a black box where the output is the graph and (ii) those preparing the graph to guide the materialized view selection process. In this category, the graph generation is based on individual query plans, an approach that does not scale, especially with the explosion of Big Data applications requiring large number of complex queries with high interaction. To ensure a scalable solution, this work proposes a new technique to generate a global processing plan without using individual plans by borrowing techniques used in the electronic design automation (EDA) domain. This paper first presents a rich state of art regarding the PVS and a classification of the most important existing work. Secondly, an analogy between the MQO problem and the EDA domain, in which large circuits are manipulated, is established. Thirdly, it proposes to model the problem with hypergraphs which are massively used to design and test integrated circuits. Fourthly, it proposes a deterministic algorithm to select materialized views using the global processing plan. Finally, experiments are conducted to show the scalability of our approach.

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Xiahou, Jian Bing, Qian Qian Wei, Xiao Na Deng, and Xiao Wei Liu. "Research and Optimization of Materialized Views Selection Algorithm Based on the Data Warehouse." Advanced Materials Research 926-930 (May 2014): 3165–70. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3165.

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Materialized view is an effective mothed for improving the efficiency of queries in data warehouse system,and materialized views selection problem is one of the most important decisions in designing a data warehouse.This paper begins with a brief introduction to materialized view and study of the existing materialized viewalgorithm.Then in order toselect an appropriate set of views that minimizes total query response timeand the cost of maintaining the selected views under a limitedstorage space, a hybrid algorithm combined with the advantages of ant colony algorithm and immune genetic algorithm is proposed.Inaddition,analyze the shortcomings of this algorithm and propose some improvement ideas, which optimize the efficiency of algorithm to some extent.

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Dahiya, Naveen, Vishal Bhatnagar, and Manjeet Singh. "Efficient Materialized View Selection for Multi-Dimensional Data Cube Models." International Journal of Information Retrieval Research 6, no. 3 (July 2016): 52–74. http://dx.doi.org/10.4018/ijirr.2016070104.

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Decision Support Systems help managers to make intelligent decisions by throwing complex queries on large databases. The response time to queries is a very crucial factor in governing the quality of decision support systems. The response time can be greatly improved by using query optimization techniques. A powerful query optimization technique selects only some of the views and not all views for materialization. The authors in this paper present a refined greedy selection approach using forward references to give better materialized view selection. The approach works on lattice framework of data that is capable enough to show inter dependencies of data. The choice of materialized views using the proposed approach gives a better trade off in terms of space/benefits, which is proved from the experimental results. The refined greedy selection approach is independent of space constraint and depends on number of passes entered by the user. The view selection is further enhanced by including space constraints to the results of greedy and refined greedy approach using knapsack implementation.

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Tang, Chang, Xinzhong Zhu, Xinwang Liu, and Lizhe Wang. "Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5101–8. http://dx.doi.org/10.1609/aaai.v33i01.33015101.

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Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

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Close, Caroline, and Camille Kelbel. "Whose primaries? Grassroots’ views on candidate selection procedures." Acta Politica 54, no. 2 (April 5, 2018): 268–94. http://dx.doi.org/10.1057/s41269-018-0086-0.

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Tarhan, Ozge, and Fatma Susar. "Teacher Candidates’ Views on Public Personnel Selection Examination." Procedia - Social and Behavioral Sciences 186 (May 2015): 874–81. http://dx.doi.org/10.1016/j.sbspro.2015.04.059.

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Fang, Yong, Lin Bo, Daping Zhao, and Shouyang Wang. "Fuzzy Views on Black-Litterman Portfolio Selection Model." Journal of Systems Science and Complexity 31, no. 4 (November 29, 2017): 975–87. http://dx.doi.org/10.1007/s11424-017-6330-2.

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Haider, Mohammad, and T. V. Vijay Kumar. "Materialised views selection using size and query frequency." International Journal of Value Chain Management 5, no. 2 (2011): 95. http://dx.doi.org/10.1504/ijvcm.2011.042071.

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Mailis, Theofilos, Yannis Kotidis, Stamatis Christoforidis, Evgeny Kharlamov, and Yannis Ioannidis. "View selection over knowledge graphs in triple stores." Proceedings of the VLDB Endowment 14, no. 13 (September 2021): 3281–94. http://dx.doi.org/10.14778/3484224.3484227.

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Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.

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Kumar, Akshay, and T. V. Vijay Kumar. "View Materialization Over Big Data." International Journal of Data Analytics 2, no. 1 (January 2021): 61–85. http://dx.doi.org/10.4018/ijda.2021010103.

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Advances in technology have resulted in the generation of a large volume of heterogeneous big data for large enterprises engaged in e-commerce, healthcare, education, etc. This is being created at a rapid rate but is low in its veracity. This big data includes large sets of semi-structured and unstructured data and is stored over a distributed file system (DFS). This data can be processed in a fault tolerant manner using several frameworks, tools, and advanced database technologies. Big data can provide important information, which can be used for business decision making. View materialization, which has been widely studied for structured databases or data warehouse, has been extended to big data to enhance efficiency of big data query processing. This paper focuses on the selection of big data views for materialization. The big data views can be identified by extracting a set of query attributes from the set of query workload of an enterprise. The query attributes are interrelated resulting in the creation of alternate access paths for query evaluation. The cost of query processing using big data views involves the integrity of different data types of heterogeneous big data, frequency of queries, change in the size of big data, selected sets of big data materialized views, and updates on big data and these sets of materialized views. The cost of query processing is computed using the stored size of big data views on the DFS system, which is a consistent processing framework of DFS. A big data view selection algorithm that is capable of selecting views from structured, semi-structured, and unstructured data has been proposed in this paper. The proposed algorithm would select big data views that would result in faster processing of most user queries resulting in efficient decision making.

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Mokhtarian, Farzin, and Sadegh Abbasi. "Robust automatic selection of optimal views in multi-view free-form object recognition." Pattern Recognition 38, no. 7 (July 2005): 1021–31. http://dx.doi.org/10.1016/j.patcog.2004.11.021.

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Pisano, Valentina Indelli, Michele Risi, and Genoveffa Tortora. "How reduce the View Selection Problem through the CoDe Modeling." Journal on Advances in Theoretical and Applied Informatics 2, no. 2 (December 21, 2016): 19. http://dx.doi.org/10.26729/jadi.v2i2.2090.

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Big Data visualization is not an easy task due to the sheer amount of information contained in data warehouses. Then the accuracy on data relationships in a representation becomes one of the most crucial aspects to perform business knowledge discovery. A tool that allows to model and visualize information relationships between data is CoDe, which by processing several queries on a data-mart, generates a visualization of such data. However on a large data warehouse, the computation of these queries increases the response time by the query complexity. A common approach to speed up data warehousing is precompute a set of materialized views, store in the warehouse and use them to compute the workload queries. The goal and the objectives of this paper are to present a new process exploiting the CoDe modeling through determining the minimal number of required OLAP queries and to mitigate the problem of view selection, i.e., select the optimal set of materialized views. In particular, the proposed process determines the minimal number of required OLAP queries, creates an ad hoc lattice structure to represent them, and selects on such structure the views to be materialized taking into account an heuristic based on the processing time cost and the view storage space. The results of an experiment on a real data warehouse show an improvement in the range of 36-98% with respect the approach that does not consider materialized views, and 7% wrt. an approach that exploits them. Moreover, we have shown how the results are affected by the lattice structure.

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Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-II." Information Resources Management Journal 34, no. 2 (April 2021): 1–28. http://dx.doi.org/10.4018/irmj.2021040101.

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Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

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Kumar, Akshay, and T. V. Vijay Kumar. "A Multi-Objective Approach to Big Data View Materialization." International Journal of Knowledge and Systems Science 12, no. 2 (April 2021): 17–37. http://dx.doi.org/10.4018/ijkss.2021040102.

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Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.

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Khan, Shahid, Nazeer Muhammad, Shabieh Farwa, Tanzila Saba, and Zahid Mahmood. "Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC." Symmetry 11, no. 4 (April 1, 2019): 454. http://dx.doi.org/10.3390/sym11040454.

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The Multi-View extension of High Efficiency Video Coding (MV-HEVC) has improved the coding efficiency of multi-view videos, but this comes at the cost of the extra coding complexity of the MV-HEVC encoder. This coding complexity can be reduced by efficiently reducing time-consuming encoding operations. In this work, we propose two methods to reduce the encoder complexity. The first one is Early Coding unit Splitting (ECS), and the second is the Efficient Reference Picture Selection (ERPS) method. In the ECS method, the decision of Coding Unit (CU) splitting for dependent views is made on the CU splitting information obtained from the base view, while the ERPS method for dependent views is based on selecting reference pictures on the basis of the temporal location of the picture being encoded. Simulation results reveal that our proposed methods approximately reduce the encoding time by 58% when compared with HTM (16.2), the reference encoder for MV-HEVC.

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Kumar, Amit, and T. V. Vijay Kumar. "Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization." International Journal of Reliability, Quality and Safety Engineering 24, no. 06 (November 17, 2017): 1740001. http://dx.doi.org/10.1142/s0218539317400010.

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A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.

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Zhou, Li Juan, Hai Jun Geng, and Ming Sheng Xu. "Research on Materialized View Selection in the Data Warehouse." Applied Mechanics and Materials 55-57 (May 2011): 361–66. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.361.

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Materialized view is an effective method for improving the efficiency of queries in data warehouse system, and the problem of materialized view selection is one of the most important decisions. In this paper, an algorithm was proposed to select a set of materialized views under maintenance cost constraints for the purpose of minimizing the total query processing cost; the algorithm adopts the dynamic penalty function to solve the resource constraints view selection. The experimental study shows that the algorithm has better solutions and high efficiency.

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Ben-Dor, Itsik, Gaby Weissman, Toby Rogers, Michael Slack, Augusto Pichard, Naama Ben-Dor, Hayder Hashim, Nelson Bernardo, Lowell F. Satler, and Ron Waksman. "Catheter Selection and Angiographic Views for Anomalous Coronary Arteries." JACC: Cardiovascular Interventions 14, no. 9 (May 2021): 995–1008. http://dx.doi.org/10.1016/j.jcin.2021.01.054.

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Gupta, H., and I. S. Mumick. "Selection of views to materialize in a data warehouse." IEEE Transactions on Knowledge and Data Engineering 17, no. 1 (January 2005): 24–43. http://dx.doi.org/10.1109/tkde.2005.16.

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Wilson, David Sloan. "A Critique of R.D. Alexander's Views on Group Selection." Biology & Philosophy 14, no. 3 (July 1999): 431–49. http://dx.doi.org/10.1023/a:1006577511789.

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Journal articles on the topic 'Views selection'

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